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Unsupervised clustering algorithms improve the reproducibility of dynamic contrast-enhanced magnetic resonance imaging pulmonary perfusion quantification in muco-obstructive lung diseases

BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows the assessment of pulmonary perfusion, which may play a key role in the development of muco-obstructive lung disease. One problem with quantifying pulmonary perfusion is the high variability of metrics. Quantifying the e...

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Main Authors: Konietzke, Marilisa (Author) , Triphan, Simon M. F. (Author) , Eichinger, Monika (Author) , Bossert, Sebastian (Author) , Heller, Hartmut (Author) , Wege, Sabine (Author) , Eberhardt, Ralf (Author) , Puderbach, Michael (Author) , Kauczor, Hans-Ulrich (Author) , Heußel, Gudula (Author) , Heußel, Claus Peter (Author) , Risse, Frank (Author) , Wielpütz, Mark Oliver (Author)
Format: Article (Journal)
Language:English
Published: 24 October 2022
In: Frontiers in medicine
Year: 2022, Volume: 9, Pages: 1-12
ISSN:2296-858X
DOI:10.3389/fmed.2022.1022981
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.3389/fmed.2022.1022981
Verlag, kostenfrei, Volltext: https://www.frontiersin.org/articles/10.3389/fmed.2022.1022981
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Author Notes:Marilisa Konietzke, Simon M. F. Triphan, Monika Eichinger, Sebastian Bossert, Hartmut Heller, Sabine Wege, Ralf Eberhardt, Michael U. Puderbach, Hans-Ulrich Kauczor, Gudula Heußel, Claus P. Heußel, Frank Risse and Mark O. Wielpütz
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Summary:BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows the assessment of pulmonary perfusion, which may play a key role in the development of muco-obstructive lung disease. One problem with quantifying pulmonary perfusion is the high variability of metrics. Quantifying the extent of abnormalities using unsupervised clustering algorithms in residue function maps leads to intrinsic normalization and could reduce variability.PurposeWe investigated the reproducibility of perfusion defects in percent (QDP) in clinically stable patients with cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD).Methods15 CF (29.3 ± 9.3y, FEV1%predicted = 66.6 ± 15.8%) and 20 COPD (66.5 ± 8.9y, FEV1%predicted = 42.0 ± 13.3%) patients underwent DCE-MRI twice 1 month apart. QDP, pulmonary blood flow (PBF), and pulmonary blood volume (PBV) were computed from residue function maps using an in-house quantification pipeline. A previously validated MRI perfusion score was visually assessed by an expert reader.ResultsOverall, mean QDP, PBF, and PBV did not change within 1 month, except for QDP in COPD (p < 0.05). We observed smaller limits of agreement (± 1.96 SD) related to the median for QDP (CF: ± 38%, COPD: ± 37%) compared to PBF (CF: ± 89%, COPD: ± 55%) and PBV (CF: ± 55%, COPD: ± 51%). QDP correlated moderately with the MRI perfusion score in CF (r = 0.46, p < 0.05) and COPD (r = 0.66, p < 0.001). PBF and PBV correlated poorly with the MRI perfusion score in CF (r =−0.29, p = 0.132 and r =−0.35, p = 0.067, respectively) and moderately in COPD (r =−0.57 and r =−0.57, p < 0.001, respectively).ConclusionIn patients with muco-obstructive lung diseases, QDP was more robust and showed a higher correlation with the MRI perfusion score compared to the traditionally used perfusion metrics PBF and PBV.
Item Description:Gesehen am 19.12.2022
Physical Description:Online Resource
ISSN:2296-858X
DOI:10.3389/fmed.2022.1022981

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